Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Bartolomé-Tomás, Almudena; Sánchez-Reolid, Roberto; Fernández-Sotos, Alicia; Postigo, José Miguel Latorre; Fernández-Caballero, Antonio

doi:10.3390/s20174788

Cited by 17 publications

(11 citation statements)

References 64 publications

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“…Valence refers to pleasantness, i.e., the degree of positive or negative affect, whereas arousal refers to the energetic component of the emotion (alertness). Research has consistently linked skin conductance to arousal (Christopoulos et al, 2019;Bartolomé-Tomás et al, 2020). Also, other types of physiological responses have been found to generally map better on arousal rather than valence (Mauss and Robinson, 2009).…”

Section: Introductionmentioning

confidence: 99%

A Review on the Role of Affective Stimuli in Event-Related Frontal Alpha Asymmetry

Sabu

Stuldreher

Kaneko³

et al. 2022

Front. Comput. Sci.

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Frontal alpha asymmetry refers to the difference between the right and left alpha activity over the frontal brain region. Increased activity in the left hemisphere has been linked to approach motivation and increased activity in the right hemisphere has been linked to avoidance or withdrawal. However, research on alpha asymmetry is diverse and has shown mixed results, which may partly be explained by the potency of the used stimuli to emotionally and motivationally engage participants. This review gives an overview of the types of affective stimuli utilized with the aim to identify which stimuli elicit a strong approach-avoidance effect in an affective context. We hope this contributes to better understanding of what is reflected by alpha asymmetry, and in what circumstances it may be an informative marker of emotional state. We systematically searched the literature for studies exploring event-related frontal alpha asymmetry in affective contexts. The search resulted in 61 papers, which were categorized in five stimulus categories that were expected to differ in their potency to engage participants: images & sounds, videos, real cues, games and other tasks. Studies were viewed with respect to the potency of the stimuli to evoke significant approach-avoidance effects on their own and in interaction with participant characteristics or condition. As expected, passively perceived stimuli that are multimodal or realistic, seem more potent to elicit alpha asymmetry than unimodal stimuli. Games, and other stimuli with a strong task-based component were expected to be relatively engaging but approach-avoidance effects did not seem to be much clearer than the studies using perception of videos and real cues. While multiple factors besides stimulus characteristics determine alpha asymmetry, and we did not identify a type of affective stimulus that induces alpha asymmetry highly consistently, our results indicate that strongly engaging, salient and/or personally relevant stimuli are important to induce an approach-avoidance effect.

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Section: Introductionmentioning

confidence: 99%

A Review on the Role of Affective Stimuli in Event-Related Frontal Alpha Asymmetry

Sabu

Stuldreher

Kaneko³

et al. 2022

Front. Comput. Sci.

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“…As the field of machine learning has evolved over the last decade, these algorithms have been used to train models for predicting various outcomes based on EDA data or multi-sensor data including EDA. Examples from works published within the last few years include classification of epileptic seizures (Zsom A and et al, 2019), differentiation of sensory responses for children with autism spectrum disorder (Raya MA et al, 2020), identification of cognitive tasks (Posada-Quintero HF and Bolkhovsky JB, 2019), pain assessment (Susam BT et al, 2018;Posada-Quintero HF et al, 2021;Aqajari SAH et al, 2021), emotion recognition (Al Machot F et al, 2019;Sharma V et al, 2019;Ganapathy N et al, 2020), assessment of emotional engagement (Di Lascio E et al, 2018), Stress detection (Amalan S and et al, 2018;Zontone P et al, 2019;Pakarinen T et al, 2019;Anusha AS et al, 2020;Sánchez-Reolid R et al, 2020;Greco A and et al, 2021), cognitive load measurement (Romine WL et al, 2020), detection of major depressive disorder (Kim AY et al, 2018), and arousal detection from music ( Bartolomé-Tomás A et al, 2020). Among these studies, high performances in classification accuracies have been reported ranging from 64% to 95% depending on the dataset, classification problem and difficulty A c c e p t e d M a n u s c r i p t (binary classification is for instance less challenging than multiple levels of stress).…”

Section: Machine Learningmentioning

confidence: 99%

Current trends and opportunities in the methodology of electrodermal activity measurement

Tronstad¹,

Amini²,

Bach³

et al. 2022

Physiol. Meas.

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Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

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“…We believe that higher quality data, collected in an in-person setting, might give additional insights into the combination of individual features. Additionally, the use of physiological measures, such as Electroencephalography (EEG) or Electrodermal activity (EDA) as objective markers of engagement during music listening has recently revealed promising results [29,30,31]. Future studies should address the overlap between subjective tension ratings and objective neural data.…”

Section: Limitationsmentioning

confidence: 99%

TenseMusic: An automatic prediction model for musical tension

Barchet¹,

Rimmele²,

Pelofi³

2022

Preprint

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The perception of tension and release dynamics constitutes one of the essential aspects of music listening. However, modeling musical tension to predict perception of listeners has been a challenge to researchers. Seminal work demonstrated that tension reported continuously by listeners can be accurately predicted from a discrete set of musical features, combining them into a weighted sum of slopes reflecting their combined dynamics over time. However, this model lacks an automatic pipeline for feature extraction that would make it widely accessible to researchers in the field. Here, we propose an updated version of a predictive tension model that operates using a musical audio as the only input. Using state-of-the-art music information retrieval (MIR) methods, we automatically extract a set of five features (i.e., loudness, pitch height, dissonance, tempo, and onset frequency) to use as predictors for musical tension. The algorithm was trained to best predict behavioral tension ratings collected on a variety of pieces, and its performance was tested by assessing the correlation between the predicted tension and unseen continuous behavioral tension ratings. We hope that providing the research community with an open-source algorithm for predicting musical tension will motivate further work in the music cognition field for elucidating tension dynamics and its neural and cognitive correlates for various musical genres and cultures.

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Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Cited by 17 publications

References 64 publications

A Review on the Role of Affective Stimuli in Event-Related Frontal Alpha Asymmetry

A Review on the Role of Affective Stimuli in Event-Related Frontal Alpha Asymmetry

Current trends and opportunities in the methodology of electrodermal activity measurement

TenseMusic: An automatic prediction model for musical tension

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